RTX 5060 Ti 16GBGünstige Wahl16 GB VRAM (+4)
B
Adds memory headroom for longer context windows and future model growth.44.5 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $449 MSRP
Can Llama 3.2 11B Vision run on RTX 4070 12GB?
YES — Tight Fit
B67Good
Estimated from fit model
Llama 3.2 11B Vision needs ~11.1 GB VRAM. RTX 4070 12GB has 12.0 GB. With Q4_K_M quantization, expect ~61 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 11.1 GB, 60.6 tok/s, Tight fit
11.1 GB required12.0 GB available
93% VRAM used
Fit status
Tight fit
Decode
60.6 tok/s
TTFT
3197 ms
Safe context
16K
Memory
11.1 GB / 12.0 GB
Memory breakdown
Weights6.7 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelsLlama 3.2 11B Vision on RTX 4070 12GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 60.6 tok/s decode · 3.2s TTFT (warm) · 151 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 60.6 tok/s | 1744 ms | 16K |
| Coding | B | Tight fit | 60.6 tok/s | 3197 ms | 16K |
| Agentic Coding | B | Very compromised (needs ~0.5 GB host RAM) | 38.3 tok/s | 7357 ms | 16K |
| Reasoning | B | Tight fit | 60.6 tok/s | 3778 ms | 16K |
| RAG | B | Very compromised (needs ~0.5 GB host RAM) | 38.3 tok/s | 9196 ms | 16K |
Inference speed
Llama 3.2 11B Vision inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Llama 3.2 11B Vision at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~154 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 154.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 122.7 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 110.7 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 105.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 97.9 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 89.2 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 74.3 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 70.5 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 60.6 | Tight |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 48.5 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 48.5 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 38.5 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 38.1 | Tight |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 35.2 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 29.6 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 13.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How Llama 3.2 11B Vision (11B params) fits at each quantization level on RTX 4070 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.3 GB | Low | B65 |
Q3_K_S | 3 | 5.4 GB | Low | B67 |
NVFP4 | 4 | 6.2 GB | Medium | B67 |
Q4_K_M | 4 | 6.7 GB | Medium | B66 |
Q5_K_M | 5 | 7.9 GB | High | B66 |
Q6_KBest for your GPU | 6 | 9.0 GB | High | B66 |
Q8_0 | 8 | 11.8 GB | Very High | F0 |
F16 | 16 | 22.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run Llama 3.2 11B Vision on your machine.
Run
ollama run llama3.2-vision:11bUpgrade-Optionen
Hardware, die Llama 3.2 11B Vision gut ausführt
RTX 5060 Ti 16GBGünstige WahlRTX 4060 Ti 16GBBestes Preis-Leistungs-Verhältnis16 GB VRAM (+4)
B
Adds memory headroom for longer context windows and future model growth.33.7 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $499 MSRP
RTX 2000 Ada 16GBNVIDIA-Upgrade16 GB VRAM (+4)
B
Adds memory headroom for longer context windows and future model growth.35.1 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $625 MSRP
Frequently asked questions
Can RTX 4070 12GB run Llama 3.2 11B Vision?
Yes, RTX 4070 12GB can run Llama 3.2 11B Vision with a B grade (Tight fit). Expected decode speed: 60.6 tok/s.
How much VRAM does Llama 3.2 11B Vision need?
Llama 3.2 11B Vision (11B parameters) requires approximately 11.1 GB of memory with Q4_K_M quantization.
What is the best quantization for Llama 3.2 11B Vision?
The recommended quantization for Llama 3.2 11B Vision is Q4_K_M, which balances quality and memory efficiency.
What speed will Llama 3.2 11B Vision run at on RTX 4070 12GB?
On RTX 4070 12GB, Llama 3.2 11B Vision achieves approximately 60.6 tokens per second decode speed with a time-to-first-token of 3197ms using Q4_K_M quantization.
Can RTX 4070 12GB run Llama 3.2 11B Vision for coding?
For coding workloads, Llama 3.2 11B Vision on RTX 4070 12GB receives a B grade with 60.6 tok/s and 16K context.
What context window can Llama 3.2 11B Vision use on RTX 4070 12GB?
On RTX 4070 12GB, Llama 3.2 11B Vision can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
What should I upgrade first if Llama 3.2 11B Vision feels slow on RTX 4070 12GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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<iframe src="https://willitrunai.com/embed/llama-3.2-11b-vision-on-rtx-4070-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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